Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "220" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 28 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460011 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.959338 | -1.089869 | 0.343963 | -0.701305 | -0.502513 | 1.210988 | 3.068948 | -1.273613 | 0.5951 | 0.5965 | 0.3398 | nan | nan |
| 2460010 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.997529 | -0.877796 | 0.753394 | -0.345508 | -0.407132 | -1.484907 | 2.275375 | -1.176869 | 0.6074 | 0.6123 | 0.3436 | nan | nan |
| 2460009 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.091287 | -0.988927 | 0.467290 | -0.707560 | -0.394162 | 0.491395 | 3.176900 | -0.955781 | 0.6104 | 0.6159 | 0.3483 | nan | nan |
| 2460008 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.149961 | -0.899139 | 0.569559 | -0.685523 | -0.265950 | -0.722386 | 0.581890 | -1.479719 | 0.6452 | 0.6540 | 0.3153 | nan | nan |
| 2460007 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.180335 | -0.918696 | 0.253941 | -0.691290 | -0.924852 | -0.790938 | 3.270795 | -1.262921 | 0.6144 | 0.6200 | 0.3330 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 0.08% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5559 | 0.5733 | 0.3301 | nan | nan |
| 2459998 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.968438 | -0.896528 | 0.306026 | -0.435667 | -1.031540 | -0.824277 | 2.324691 | -1.116092 | 0.5967 | 0.6029 | 0.3736 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.028087 | -1.120850 | 0.557586 | -0.439013 | -0.694714 | -0.762618 | 4.842971 | -1.502833 | 0.6068 | 0.6143 | 0.3789 | nan | nan |
| 2459996 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.959736 | -0.878356 | 0.368063 | -0.577994 | -0.639897 | -0.135671 | 1.519532 | -0.954852 | 0.6203 | 0.6234 | 0.3880 | nan | nan |
| 2459995 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.122318 | -1.278232 | 0.428403 | -0.554814 | -0.677752 | -0.594417 | 2.044931 | -1.029957 | 0.6103 | 0.6168 | 0.3761 | nan | nan |
| 2459994 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.371996 | -1.240488 | 0.448843 | -0.516203 | -0.721297 | -0.723693 | 2.730496 | -1.266759 | 0.6048 | 0.6099 | 0.3720 | nan | nan |
| 2459993 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.393459 | -1.257258 | 0.670549 | -0.344246 | -0.428961 | -0.230503 | 2.343002 | -1.031469 | 0.5788 | 0.6010 | 0.3878 | nan | nan |
| 2459991 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.330604 | -1.249101 | 0.591433 | -0.332089 | -0.433969 | 0.008065 | 2.706850 | -1.132355 | 0.6111 | 0.6071 | 0.3795 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.067534 | -0.797568 | 0.602406 | -0.190485 | -0.511681 | 0.360980 | 2.976001 | -1.558138 | 0.6079 | 0.6081 | 0.3770 | nan | nan |
| 2459989 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.217556 | -0.881101 | 0.746169 | -0.221728 | -0.645469 | -0.286822 | 2.731015 | -1.165872 | 0.6013 | 0.6072 | 0.3789 | nan | nan |
| 2459988 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.534578 | -0.988266 | 0.588773 | -0.226566 | -0.860727 | -0.008199 | 2.769722 | -0.837969 | 0.6047 | 0.6099 | 0.3703 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.275868 | -0.877382 | 0.480807 | -0.521095 | -0.895363 | -0.989896 | 4.717751 | -1.122993 | 0.6130 | 0.6148 | 0.3680 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.267346 | -1.078877 | 0.537753 | -0.364619 | -0.768421 | -0.682836 | 2.480956 | -1.086350 | 0.6265 | 0.6355 | 0.3399 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.501961 | -1.349097 | 0.397616 | -0.516652 | -0.792660 | -1.196529 | 4.792693 | -1.285368 | 0.6131 | 0.6153 | 0.3774 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.490062 | -1.549202 | 0.465774 | -0.463928 | -0.794510 | -0.769239 | 1.240078 | -1.400270 | 0.6288 | 0.6343 | 0.3550 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.449972 | -1.318168 | 0.468131 | -0.307672 | -0.770029 | -0.952984 | 1.782750 | -1.225189 | 0.6357 | 0.6527 | 0.3207 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.063694 | -1.045972 | -0.081081 | -0.736869 | -1.009430 | -1.214156 | -0.609840 | -1.456279 | 0.6816 | 0.6783 | 0.2945 | nan | nan |
| 2459981 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.116165 | -0.934637 | 0.654959 | -0.123751 | -0.459886 | 0.326358 | 3.156786 | -0.717884 | 0.6117 | 0.6171 | 0.3740 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.384775 | -1.134728 | 0.269048 | -0.636834 | -1.127406 | -0.983478 | 0.326788 | -1.383337 | 0.6486 | 0.6520 | 0.3116 | nan | nan |
| 2459979 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.206888 | -1.253988 | 0.365136 | -0.540783 | -0.738999 | -0.786965 | 2.497206 | -1.406737 | 0.6027 | 0.6123 | 0.3747 | nan | nan |
| 2459978 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.222083 | -1.018827 | 0.528236 | -0.384512 | -0.625921 | -0.216704 | 3.034911 | -1.441469 | 0.6033 | 0.6112 | 0.3820 | nan | nan |
| 2459977 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.235503 | -1.251706 | 0.336906 | -0.581792 | -0.455644 | -1.088447 | 2.469230 | -1.601766 | 0.5692 | 0.5767 | 0.3422 | nan | nan |
| 2459976 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.289856 | -1.105945 | 0.396211 | -0.444401 | -0.647006 | 0.169320 | 2.572432 | -1.245329 | 0.6100 | 0.6176 | 0.3728 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 3.068948 | -0.959338 | -1.089869 | 0.343963 | -0.701305 | -0.502513 | 1.210988 | 3.068948 | -1.273613 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.275375 | -0.997529 | -0.877796 | 0.753394 | -0.345508 | -0.407132 | -1.484907 | 2.275375 | -1.176869 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 3.176900 | -1.091287 | -0.988927 | 0.467290 | -0.707560 | -0.394162 | 0.491395 | 3.176900 | -0.955781 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 0.581890 | -0.899139 | -1.149961 | -0.685523 | 0.569559 | -0.722386 | -0.265950 | -1.479719 | 0.581890 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 3.270795 | -1.180335 | -0.918696 | 0.253941 | -0.691290 | -0.924852 | -0.790938 | 3.270795 | -1.262921 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.324691 | -0.968438 | -0.896528 | 0.306026 | -0.435667 | -1.031540 | -0.824277 | 2.324691 | -1.116092 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 4.842971 | -1.028087 | -1.120850 | 0.557586 | -0.439013 | -0.694714 | -0.762618 | 4.842971 | -1.502833 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 1.519532 | -0.959736 | -0.878356 | 0.368063 | -0.577994 | -0.639897 | -0.135671 | 1.519532 | -0.954852 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.044931 | -1.122318 | -1.278232 | 0.428403 | -0.554814 | -0.677752 | -0.594417 | 2.044931 | -1.029957 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.730496 | -1.371996 | -1.240488 | 0.448843 | -0.516203 | -0.721297 | -0.723693 | 2.730496 | -1.266759 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.343002 | -1.393459 | -1.257258 | 0.670549 | -0.344246 | -0.428961 | -0.230503 | 2.343002 | -1.031469 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.706850 | -1.330604 | -1.249101 | 0.591433 | -0.332089 | -0.433969 | 0.008065 | 2.706850 | -1.132355 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.976001 | -0.797568 | -1.067534 | -0.190485 | 0.602406 | 0.360980 | -0.511681 | -1.558138 | 2.976001 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.731015 | -0.881101 | -1.217556 | -0.221728 | 0.746169 | -0.286822 | -0.645469 | -1.165872 | 2.731015 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.769722 | -0.988266 | -1.534578 | -0.226566 | 0.588773 | -0.008199 | -0.860727 | -0.837969 | 2.769722 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 4.717751 | -1.275868 | -0.877382 | 0.480807 | -0.521095 | -0.895363 | -0.989896 | 4.717751 | -1.122993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.480956 | -1.078877 | -1.267346 | -0.364619 | 0.537753 | -0.682836 | -0.768421 | -1.086350 | 2.480956 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 4.792693 | -1.349097 | -1.501961 | -0.516652 | 0.397616 | -1.196529 | -0.792660 | -1.285368 | 4.792693 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 1.240078 | -1.490062 | -1.549202 | 0.465774 | -0.463928 | -0.794510 | -0.769239 | 1.240078 | -1.400270 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 1.782750 | -1.449972 | -1.318168 | 0.468131 | -0.307672 | -0.770029 | -0.952984 | 1.782750 | -1.225189 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Shape | -0.063694 | -0.063694 | -1.045972 | -0.081081 | -0.736869 | -1.009430 | -1.214156 | -0.609840 | -1.456279 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 3.156786 | -0.934637 | -1.116165 | -0.123751 | 0.654959 | 0.326358 | -0.459886 | -0.717884 | 3.156786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 0.326788 | -1.134728 | -1.384775 | -0.636834 | 0.269048 | -0.983478 | -1.127406 | -1.383337 | 0.326788 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.497206 | -1.206888 | -1.253988 | 0.365136 | -0.540783 | -0.738999 | -0.786965 | 2.497206 | -1.406737 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 3.034911 | -1.018827 | -1.222083 | -0.384512 | 0.528236 | -0.216704 | -0.625921 | -1.441469 | 3.034911 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.469230 | -1.235503 | -1.251706 | 0.336906 | -0.581792 | -0.455644 | -1.088447 | 2.469230 | -1.601766 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 220 | N18 | RF_maintenance | ee Temporal Discontinuties | 2.572432 | -1.105945 | -1.289856 | -0.444401 | 0.396211 | 0.169320 | -0.647006 | -1.245329 | 2.572432 |